Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics
Comparing the Diagnostic Classification Accuracy of iTRAQ, Peak Area, Spectral-Counting, and emPAI Methods for Relative Quantification in Expression Proteomics
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DOI：
10.1021/acs.jproteome.6b00308
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发表时间：
2016-10-01
影响因子：
4.4
通讯作者：
Thomas, Jerry R.
中科院分区：
文献类型：
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作者：
Dowle, Adam A.;Wilson, Julie;Thomas, Jerry R.
诊断分类的准确性在表达蛋白质组学中至关重要，以确保尽可能多的真实差异被确定为可接受的假阳性率。我们将iTRAQ的诊断准确性与三种无标记方法（峰面积、光谱计数和emPAI）进行比较，以使用加尖蛋白组标准进行相对定量。我们首次验证了emPAI用于样本间相对量化，并发现了四种量化方法之间的明显差异，这些方法在设计实验时可以考虑。光谱计数被观察到在所有方面都表现得非常好。峰面积在较小的倍数差异中表现最好，并被证明能够以可接受的特异性和敏感性识别1.1倍的差异。与低丰度蛋白的无标记方法相比，iTRAQ的性能明显较差。使用iTRAQ数据集进行验证，我们还展示了一种新的iTRAQ分析机制，该机制避免了在显著性检验中使用比率，并且优于常见的商业替代方案。
Diagnostic classification accuracy is critical in expression proteomics to ensure that as many true differences as possible are identified with acceptable false-positive rates. We present a comparison of the diagnostic accuracy of iTRAQ with three label-free methods, peak area, spectral counting, and emPAI, for relative quantification using a spiked proteome standard. We provide the first validation of emPAI for intersample relative quantification and find clear differences among the four quantification approaches that could be considered when designing an experiment. Spectral counting was observed to perform surprisingly well in all regards. Peak area performed best for smaller fold differences and was shown to be capable of discerning a 1.1-fold difference with acceptable specificity and sensitivity. The performance of iTRAQ was dramatically worse than the label-free methods with low abundance proteins. Using the iTRAQ data set for validation, we also demonstrate a novel iTRAQ analysis regime that avoids the use of ratios in significance testing and outperforms a common commercial alternative.